PRML (Partial Response Maximum Likelihood) has been adopted as a data detection method to provide information storage media (hereinafter, simply “storage media”) with higher recording densities. In PRML, waveform equalization is necessary to produce from the storage medium a reproduced waveform having frequency characteristics similar to those ideal ones which are assumed in PR class. In addition, an adaptive equalization technique is used to adaptively update the equalization properties of waveform equalization according to property irregularities of individual storage media and reproduction properties variations caused by variations in property of a reproduction system, such as disk tilting and servo offset, which are present in the reproduction from the storage medium.
An exemplary conventional adaptive equalization technique is called LMS (Least Mean Square) scheme.
The conventional scheme technique will be described in the following with reference to FIGS. 13 and 14, taking as an example PRML data detection on the basis of PR(1,2,1) properties. FIG. 13 is a graph illustrating a relationship between a reproduced waveform and an ideal waveform assumed in accordance with PR(1,2,1) properties. In FIG. 13, u(i−2), u(i−1), u(i), . . . represent a reproduced signal pattern obtained by converting the reproduced waveform from analog to digital with a channel clock; and d(i−2), d(i−1), d(i), . . . , represent an ideal waveform signal pattern at times corresponding to u(i−2), u(i−1), u(i), . . . .
FIG. 14 is a block diagram illustrating the structure of a conventional waveform equalizing device 50. The waveform equalizing device is provided with an FIR filter (Finite impulse response filter) 52 with 3 taps (equalization coefficient or tap coefficient=c(k,i) where k=0, 1, 2), an LMS calculator circuit 53, and an ideal waveform generating circuit 54. The waveform equalizing device 50 equalize a reproduced waveform to an ideal signal waveform assumed in PR class by obtaining equalization errors from the two waveforms by LMS and adaptively varying equalization properties to reduce the equalization errors.
The waveform equalizing device 50 operates as follows: In the FIR filter 52, the reproduced signal u(i) received at a time i is delayed by delay elements having a channel time T and convolved with the tap coefficient c(k,i). The output is an equalized signal: y(i−1)=c(0,i)u(i)+c(1,i)u(i−1)+c(2,i)u(i−2). The ideal waveform generating circuit 54 generates an ideal PR(1,2,1) waveform signal d(i−1) for the reproduced signal u(i−1). The LMS calculator circuit 53 receives the ideal waveform signal d(i−1), the tap coefficient c(k,i) at the time i, the equalized signal y(i−1), and the reproduced signal pattern u(i−2), u(i−1), u(i). Upon reception of the reproduced signal u(i) at the time i, the LMS calculator circuit 53 calculates a new tap coefficient c(k,i+1) given byc(k,i+1)=c(k,i)−μ{y(i−1)−d(i−1)}u(i−k),according to which the FIR filter 52 updates the tap coefficient at a time i+1.
The above expression evaluated by the LMS calculator circuit 53 is known typically as the LMS algorithm, where μ is a constant termed a step gain. Filter theory demonstrates: if the step gain μ is set to an appropriate value, the tap coefficient c(k,i) converges to a predetermined value with recursive updates; and the mean square error E[{y(i)−d(i)}2] (E is an operator for an expectation) from the ideal waveform is the smallest in the equalization of the reproduced waveform based on the value to which the tap coefficient ultimately converges.
Another LMS-based technique is disclosed in Japanese Published Unexamined Patent Application 2000-156041 (Tokukai 2000-156041; published on Jun. 6, 2000). The technique performs adaptive equalization, targeting the reproduction impulse response after servo error adjustment as ideal reproduction properties.
Japanese Published Unexamined Patent Application 10-21651 (Tokukaihei 10-21651/1998, published on Jan. 23, 1998) discloses a technique based on an evaluation function which evaluates a reproduced waveform, not to perform waveform equalization, but to adjust, for example, sampling phase irregularities and track offsets in a reproducing operation. The technique obtains the standard deviation of a difference metric which is output by an optimal decoder and then chosen. The standard deviation is employed as the foregoing evaluation function. Adjustment is made so as to minimize the standard deviation.
However, waveform equalization generally enhances noise in high frequency range; therefore, if adaptive equalization based on an LMS algorithm is performed on a low-resolution reproduced signal (i.e., notable decay in high frequency components) due to high density recording, with an ideal waveform assumed in PR class as a target, as is done by the waveform equalizing device 50, high frequency components are greatly enhanced, and the S/N (Signal to Noise) ratio deteriorates. Thus, the equalization does not always provide the lowest error rate. In other words, the signal waveform equalized for the lowest error rate show decayed high frequency components in comparison to those assumed in PR class. The degree of the decay varies greatly from one reproduction system to another.
Therefore, for the lowest error rate, adaptive equalization needs to be performed to optimize equalization properties, taking the correlation between the equalization properties and the error rate into consideration.
The technique disclosed in Tokukai 2000-156041 mentioned above requires, prior to any other process, the generation of an ideal waveform through servo optimization. The start-up time is therefore long, and the start-up process is complex. Further, the technique employs the reproduced waveform as such in a servo optimized state, that is, a waveform before equalization, as the ideal waveform. Hence, the ideal waveform does not really have a low error rate. Adaptive equalization through this technique again does not always ensure equalization properties with the lowest error rate.
The technique disclosed in Tokukaihei 10-21651/1998 mentioned above goes no further than the adjustment of, for example, sampling phase irregularities and track offsets in a reproducing operation, falling short of equalizing the waveform. The adjustment is less effective and hence more difficult to achieve a satisfactory result in lowering the error rate than suitable waveform equalization.
Meanwhile, with digitalization of various kinds of information such as image information and sound information, amounts of digital information are dramatically increased. Accordingly, a storage medium suitable for higher capacity/higher density and a storage/reproducing device are being developed. An example of the storage medium, which is suitable for higher capacity/higher density and is superior in portability, is an optical disc.
The higher density of the optical disc deteriorates the quality of the reproduced signal read out from the optical disc. Thus, it is important to evaluate the reproduced signal so as to guarantee the quality of the reproduced signal. The evaluation of the reproduced signal is performed to check the quality of the optical disc before shipment for example, or to adjust parts of the optical disc so that the quality of the reproduced signal is optimized.
Conventionally, jitter has been often used as an evaluation value of the reproduced signal quality upon evaluating the optical disc. However, PRML is being adopted as a data detection method for realizing higher density storage recently. Under such condition, jitter which represents irregularities in a direction of a time base is not suitable as an evaluation value. Further, a bit error rate of a data detection result that has been obtained by PRML is used as the evaluation value, but this brings about many disadvantages as follows: a large number of sample bits are required upon measurement, and defects caused by flaws of a disk tend to influence the evaluation, and other similar disadvantages are brought about.
In such background, an evaluation method, called SAM (Sequenced Amplitude Margin), by which quality of a reproduced signal is evaluated, is proposed (T. Perkins, “A Window-Margin-Like Procedure for Evaluating PRML Channel Performance”; IEEE Transactions on Magnetics, Vol. 31, No. 2, 1995, p1109–1114).
Here, a concept of SAM is described with reference to FIGS. 21 and 22. As an example, the following describes a case where a reproduced signal of a bit pattern that has been recorded on the basis of d=1 (1, 7) RLL (Run Length Limited) Coding is decoded in PRML, in accordance with PR (1, 2, 1) properties.
As shown in FIG. 21, a reproduced waveform in accordance with PR(1,2,1) properties with an ideal 1T mark free from any distortion or noise has a 1:2:1 level ratio of samples for a channel clock. For a reproduced waveform from a 2T or more mark, the level ratio is obtainable from the superimposition of the reproduced waveform from a 1T mark. For example, the sample level ratio is 1:3:3:1 for the one with a 2T mark, 1:3:4:3:1 for the one with a 3T mark, and 1:3:4:4:3:1 for the one with a 4T mark. An ideal reproduced waveform can be assumed for any given bit pattern. There are five ideal sample levels (ideal sample levels): 0, 1, 2, 3, and 4.
As a technique for specifically realizing PRML decoding, Viterbi decoding is adopted. The Viterbi decoding is described as follows with reference to a trellis diagram shown in FIG. 22. In FIG. 22, S(00), S(01), S(10), and S(11) each represents a different state: for example, the state S(00) means a 0 previous bit and a 0 current bit. A line linking a state to the other is termed a “branch,” which represents a state transition: for example, a branch of S(00)-S(01) represents a “001” bit pattern. Here, the S(01)-S(10) and S(10)-S(01) branches are missing from the diagram. This is because the 010 and 101 bit patterns cannot occur due to the d=1 (1,7) RLL.
Further, in FIG. 22, each letter of α to ζ is allocated to each branch as an identifier, and an ideal waveform level expected at each state transition follows the identifier. In PR (1, 2, 1), an ideal waveform level is determined by three successive bits: v0, v1, and v2, and a value of the ideal waveform level is calculated by v0+2v1+v2. For example, when a represents a “000” bit pattern, the ideal level is 0, and when β represents a “100” bit pattern, the ideal level is 1.
In the trellis diagram, a “path” is formed by connecting continuous branches between the states. To consider all the paths generated after transiting from any one of states to another means to consider all the possible bit patterns. The most likely path, or the “correct path,” can be determined by comparing the waveform actually reproduced from the optical disc with every ideal waveform derived from the paths to find the ideal waveform that is the “closest” to the reproduced waveform, that is, the one with the least Euclidean distance from the reproduced waveform.
A Viterbi decoding procedure based on a trellis diagram will be specifically described. At any given time, there are two paths merging at each of states S(00) and S(11), whereas there is a single path coming in to each of S(01) and S(10). Of the two paths merging at S(00) and S(11), retain the one with a less Euclidean distance between the ideal waveform and the reproduced waveform; this leaves four paths each terminating at a different one of the four states at any given time.
The square of the Euclidean distance between the ideal and reproduced waveforms for a path is termed the path metric. The path metric is calculated by summing up branch metrics for all branches making up the path (the branch metric is the square of the difference between the ideal sample level of the branch and the sample level of a reproduced waveform). When a sample level of the reproduced waveform at time t is X[t] (the reproduced waveform is normalized so that an amplitude is ±2 and a central level is 2 so as to correspond to the ideal waveform of PRML), branch metrics of branches α, β, γ, δ, ε, and ζ at time t are Bα[t], Bβ[t], Bγ[t], Bδ[t], Bε[t], and Bζ[t] respectively, and path metrics of survivor paths at the states S(00), S(01), S(10), and S(11) at time t are M(00)[t], M(01)[t], M(10)[t], and M(11)[t] respectively, the branch metric is calculated in accordance with the equation (31), and the path metric is calculated in accordance with the equation (32). A process of selecting a smaller path metric from M(00)[t] and M(11)[t] corresponds to determination of a survivor path.Bα[t]=(X[t]−0)2Bβ[t]=Bγ[t]=(X[t]=−1)2Bδ[t]=(X[t]−4)2Bζ[t]=(X[t]−4)2  (31)M(00)[t]=Min{M(00)[t−1]+Bα[t],M(10)[t−1]+Bβ[t]}M(01)[t]=M(00)[t−1]+Bγ[t]M(10)[t]−M(11)[t−1]+Bδ[t]M(11)[t]=Min{M(01)t−1+Bε[t],M(11)[t−1]+Bζ[t]}(Min{m,n}=m(if m≦n):n(if m>n)  (32)
When the procedure for determining the survivor path every time the sample values of the reproduced waveform are received, a path with a greater path metrics is eliminated, so that the number of paths are gradually narrowed into one. This one is regarded as the correct path, so that the original data bit pattern is correctly reproduced.
Here, let us now consider conditions under which Viterbi decoding is correctly done. For the correct path to be ultimately chosen, the path metric must be smaller for the correct path than for other, error path every time a survivor path is determined. This condition is expressed by the equation (33).
(when the correct bit pattern is “ . . . 000”)ΔM=(M(10)[t−1]+Bβ[t])−(M(00)[t−1]+Bα[t])>0(when the correct bit pattern is “ . . . 100”)ΔM=(M(00)[t−1]+Bα[t])−(M(10)[t−1]+Bβ[t])>0(when the correct bit pattern is “ . . . 011”)ΔM=(M(11)[t−1]+Bζ[t])−(M(01)[t−1]+Bε[t])>0(when the correct bit pattern is “ . . . 111”)ΔM=(M(01)[t−1]+Bε[t])−(M(11)[t−1]+Bζ[t])>0  (33)(when the correct bit pattern is “ . . . 001” or “ . . . 110”)Because the survivor path is correctly determined, ΔM>0 always holds.
In the equation (33), ΔM is a path metric difference between two paths one of which will be the survivor path, and the difference is termed SAM. It is necessary that SAM>0 so that any error does not occur, which shows that: the less error occurs, the larger SAM becomes.
FIG. 23 is a histogram (frequency distribution) of SAM calculated from the reproduced signal of the (1,7) RLL code pattern that has been actually stored on an optical disc. As apparent from this result, SAM histogram has two waves. This is because the bit patterns are different from each other in the Euclidean distance between the correct path and the error path in a case of calculating SAM for all the reproduced signals.
That is, as shown in FIG. 24, the SAM histogram of a totally noise-free ideal reproduced signal on the basis of the (1,7) RLL code pattern takes discrete values (ideal values) of 6, 10, 14, 18, 20, 24, 28, 32, 36, . . . . The frequencies of the ideal values vary because the number of bit patterns vary from one ideal value to another, and the bit patterns occur at different probabilities in (1,7) RLL coding. Because of the presence of various kinds of noise in the reproduced signal, there are irregularities in the ideal values. As a result, the distribution is such that a plurality of distributions are superimposed as shown in FIG. 23.
The SAM histogram has such characteristics, and shows a distribution which largely differs from a normal distribution. Thus, even if a standard deviation is simply calculated from the distribution, this has little correlativity with the bit error. In a technique recited in Japanese Published Unexamined Patent Application 10-21651 (Tokukaihei 10-21651/1998, published on Jan. 23, 1998), SAM histogram is generated by selecting only such a pattern that the noise is highly likely to cause SAM<0 and SAM ideal value=6, and a standard deviation is calculated as an index for indicating the irregularities in the pattern, so as to evaluate the quality of the reproduced signal.
However, the conventional arrangement brings about the following disadvantages: it is necessary to realize the foregoing Viterbi decoding circuit to calculate SAM, and it is necessary to make a new arrangement for determining which ΔM should be selected from the equation (33) as SAM corresponding to the correct bit pattern, so that the circuit is complicated.
Further, in case of designing a system of a reproducing device for a storage medium by using an existing signal processing LSI whose PRML function has been black-boxed, it is impossible to calculate SAM.